Synergistic use of handcrafted and deep learning features for tomato leaf disease classification by Mohamed Bouni, Badr Hssina, Khadija Douzi, Samira Douzi

“…Abstract This research introduces a Computer-Aided Diagnosis-system designed aimed at automated detections & classification of tomato leaf diseases, combining traditional handcrafted features with advanced deep learning techniques. …”
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A Lightweight Direction-Aware Network for Vehicle Detection by Luxia Yang, Yilin Hou, Hongrui Zhang, Chuanghui Zhang

“…Vehicle detection algorithms, which are essential to intelligent traffic management and control systems, have attracted growing attention. …”
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Adaptive Anomaly Detection Framework Model Objects in Cyberspace by Hasan Alkahtani, Theyazn H. H. Aldhyani, Mohammed Al-Yaari

“…The information gain method was applied to select the relevant features from the network dataset. These network features were significant to improve the classification algorithm. …”
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Exploration of geo-spatial data and machine learning algorithms for robust wildfire occurrence prediction by Svetlana Illarionova, Dmitrii Shadrin, Fedor Gubanov, Mikhail Shutov, Usman Tasuev, Ksenia Evteeva, Maksim Mironenko, Evgeny Burnaev

“…The goal of this study is to explore the potential of predicting wildfire occurrences using various available environmental parameters - meteorological, geo-spatial, and anthropogenic - and machine learning (ML) algorithms. We developed a unified pipeline for data acquisition and subsequent ML-based algorithm development. …”
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Machine learning–based feature prediction of convergence zones in ocean front environments by Weishuai Xu, Lei Zhang, Hua Wang

“…Furthermore, among the input features, the turning depth emerged as a crucial determinant, contributing more than 25% to the model’s effectiveness in predicting the convergence zone’s distance. …”
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Using Electrocardiogram Signal Features and Heart Rate Variability to Predict Epileptic Attacks by Ying Jiang, Yuan Feng, Danni Lu, Lin Yang, Qun Zhang, Haiyan Yang, Ning Li

“…We used a multivariate statistical process control algorithm for abnormality detection. The presented algorithm was evaluated on a dataset consisting of 17 patients, where the obtained results show that the proposed method can predict epileptic attacks with an accuracy of 88.2%. …”
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UAV-to-Ground Target Detection Based on YOLO-DSBE by Meng Pengshuai, Wang Feng, Zhai Weiguang, Ma Xingyu, Zhao Wei

“…To address the issues of complex background, small target scale, mutual occlusion and high missed detection rate in UAV captured images, this paper proposes a ground target detection algorithm based on YOLO-DSBE.The DC-ELAN and DC-MP modules based on deformable convolution are proposed to adapt to input features of different shapes and sizes, and to improve the network’s ability to parse features in complex backgrounds; A high-resolution multi-scale detection layer is designed to boost the algorithm’s capability in extracting small target features, thereby improving the detection accuracy of minute targets. …”
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Parameter Prediction for Metaheuristic Algorithms Solving Routing Problem Instances Using Machine Learning by Tomás Barros-Everett, Elizabeth Montero, Nicolás Rojas-Morales

“…Furthermore, we present an explainability analysis to detect which features are more relevant for the prediction of suitable parameter values.…”
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A spike is a spike: On the universality of spike features in four epilepsy models by Armen Sargsyan, Pablo M. Casillas‐Espinosa, Dmitri Melkonian, Terence J. O'Brien, Gilles vanLuijtelaar

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DTFA-Net: Dynamic and Texture Features Fusion Attention Network for Face Antispoofing by Xin Cheng, Hongfei Wang, Jingmei Zhou, Hui Chang, Xiangmo Zhao, Yilin Jia

“…We proposed a dynamic information fusion structure of an interchannel attention block to fuse the magnitude and direction of optical flow to extract facial motion features. In addition, for the face detection failure of HOG algorithm under complex illumination, we proposed an improved Gamma image preprocessing algorithm, which effectively improved the face detection ability. …”
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SLPOD: superclass learning on point cloud object detection by Xiaokang Yang, Kai Zhang, Yangyue Feng, Beibei Su, Yiming Cai, Kaibo Zhang, Zhiheng Zhang

“…To tackle this challenge, we introduce SLPOD, a Superclass-based point cloud object detection algorithm. Employing a siamese network structure, SLPOD conducts unsupervised clustering of samples within the same category to enhance the extraction of individual-specific features, thereby improving detection accuracy when confronted with complex datasets. …”
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Detection of child depression using machine learning methods. by Umme Marzia Haque, Enamul Kabir, Rasheda Khanam

“…The variables of yes/no value of low correlation with the target variable (depression status) have been eliminated. The Boruta algorithm has been utilized in association with a Random Forest (RF) classifier to extract the most important features for depression detection among the high correlated variables with target variable. …”
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Obfuscated malicious traffic detection based on data enhancement by Ke Ye, Tao Zeng, Yubing Duan, Jun Han, Guoxin Zhong, Zhi Chen, Yulong Wang

“…Although existing methods combine unencrypted statistical features, e.g., average packet length, with machine learning algorithms to achieve encrypted malicious traffic detection, it is difficult to escape the influence of artificially forged noise, e.g., adding dummy packets. …”
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Detecting Unbalanced Network Traffic Intrusions With Deep Learning by S. Pavithra, K. Venkata Vikas

“…Furthermore, the Random Forest Regressor is used to ascertain the importance of features for enhancing detection accuracy and interpretability. …”
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Extracting Optimal Number of Features for Machine Learning Models in Multilayer IoT Attacks by Badeea Al Sukhni, Soumya K. Manna, Jugal M. Dave, Leishi Zhang

“…Therefore, this research aims to develop a Semi-Automated Intrusion Detection System (SAIDS) that integrates efficient feature selection, feature weighting, normalisation, visualisation, and human–machine interaction to detect and identify multilayer attacks, enhancing mitigation strategies. …”
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Time synchronization attack detection for industrial wireless network by Sichao ZHANG, Wei LIANG, Xudong YUAN, Yinlong ZHANG, Meng ZHENG

“…High-precision time synchronization is the basis for ensuring the secure and reliable transmission of industrial wireless network (IWN).Delay attacks, as a class of time synchronization attacks which cannot be solved by cryptographic techniques, seriously threaten the secure operation of IWN.Firstly, based on the in-depth analysis on the time synchronization mechanisms of IWN, three-time synchronization attack models were proposed, including the one-way full life cycle delay attack, two-way full life cycle delay attack, and one-way non-full-life cycle delay attack.Stealthier delay attacks could be realized by the attack models under the premise that target nodes were not captured.Secondly, considering the problem that existing detection algorithms are difficult to detect stealthier delay attacks without obvious changes in time features, an attack detection algorithm based on a Bayesian model was proposed that extracts four representative features, including transmission rate, transmission delay, transmission success rate and time synchronization interval.In addition, in order to ensure the accuracy of the attack detection and classification in the presence of noise interference, the noise model of wireless channel was introduced to the Bayesian feature information matrix.Experimental results show that the proposed algorithm can effectively detect three kinds of attacks in the presence of noise.…”
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Vehicle Detection and Shape Refinement Based on LiDAR by YU An, HU Dongfang, WANG Xuepeng, ZHANG Jin, ZHOU Yan, XIE Guotao, QIN Xiaohui, HU Manjiang

“…In order to solve this problem, this paper proposes a vehicle shape optimization algorithm based on point cloud cluster features, the proposed algorithm outputs vehicle detection results with PointPillars target detection algorithm. …”
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Time synchronization attack detection for industrial wireless network by Sichao ZHANG, Wei LIANG, Xudong YUAN, Yinlong ZHANG, Meng ZHENG

“…High-precision time synchronization is the basis for ensuring the secure and reliable transmission of industrial wireless network (IWN).Delay attacks, as a class of time synchronization attacks which cannot be solved by cryptographic techniques, seriously threaten the secure operation of IWN.Firstly, based on the in-depth analysis on the time synchronization mechanisms of IWN, three-time synchronization attack models were proposed, including the one-way full life cycle delay attack, two-way full life cycle delay attack, and one-way non-full-life cycle delay attack.Stealthier delay attacks could be realized by the attack models under the premise that target nodes were not captured.Secondly, considering the problem that existing detection algorithms are difficult to detect stealthier delay attacks without obvious changes in time features, an attack detection algorithm based on a Bayesian model was proposed that extracts four representative features, including transmission rate, transmission delay, transmission success rate and time synchronization interval.In addition, in order to ensure the accuracy of the attack detection and classification in the presence of noise interference, the noise model of wireless channel was introduced to the Bayesian feature information matrix.Experimental results show that the proposed algorithm can effectively detect three kinds of attacks in the presence of noise.…”
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Investigate the Use of Deep Learning in IoT Attack Detection by Mohamed Saddek Ghozlane, Adlen Kerboua, Smaine Mazouzi, Lakhdar Laimeche

“…This study contributes a comprehensive comparative analysis of deep learning models for IoT security, focusing on the effectiveness of weighted features in improving detection accuracy. The results provide valuable information for the advancement of real-time IoT attack detection systems.…”
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Optimizing Gammatone Cepstral Coefficients for Gear Fault Detection by Zrar Kh Abdul, Abdulbasit K. Al-Talabani, Wisam Hazim Gwad, Entisar Alkayal, Halgurd S. Maghdid, Safar Maghdid Asaad

“…Cepstral features, such as Gammatone Cepstral Coefficients (GTCC), have recently been applied in fault detection and diagnosis. …”
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